Airline planning benchmark problems—Part I:: Characterising networks and demand using limited data

This paper is the first of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on the analysis of airline data. Then a multi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II extends this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.     

Airline flight schedule planning under competition

This paper presents a modeling framework for airline flight schedule planning under competition. The framework generates an operational flight timetable that maximizes the airline's revenue, while ensuring efficient utilization of the airline's resources (e.g. aircraft and crew). It explicitly considers passenger demand shift due to the network-level competition with other airlines. It also considers minimizing the needless ground time of the resources. The problem is formulated in the form of a bi-level mathematical program where the upper level represents the airline scheduling decisions, while the lower level captures passenger responses in terms of itinerary choices. A solution methodology is developed which integrates a meta- heuristic search algorithm, a network competition analysis model, and a resource (e.g. aircraft and crew) tracking model. The performance of the framework is evaluated through several experiments to develop the schedule for a major U.S. airline. The results demonstrate the success of the framework to develop a competitive schedule with efficient resources.     

Improving Airline Network Robustness and Operational Reliability by Sequential Optimisation Algorithms

A sequential optimisation algorithm is developed to improve the operational reliability of airline schedules. Simulation results show that departure delays are reduced by 30% after optimisation by using extra 260 min buffer times in the schedule. This also increases the network-wide schedule reliability from 37 to 52% and an estimated delay cost saving of $20 million dollars per annum for a small airline network. The advantage of sequential optimisation is that it considers the delay/punctuality propagation in airline networks, so to prevent airlines from planning excessive buffer times to individual flights by considering aircraft rotation as a whole process.     

Integrated aircraft-path assignment and robust schedule design with cruise speed control

Assignment of aircraft types, each having different seat capacity, operational expenses and availabilities, critically affects airlines’ overall cost. In this paper, we assign fleet types to paths by considering not only flight timing and passenger demand, as commonly done in the literature, but also operational expenses, such as fuel burn and carbon emission costs associated with adjusting the cruise speed to ensure the passenger connections. In response to flight time uncertainty due to the airport congestions, we allow minor adjustments on the flight departure times in addition to cruise speed control, thereby satisfying the passenger connections at a desired service level. We model the uncertainty in flight duration via a random variable arising in chance constraints to ensure the passenger connections. Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult. To handle them, we use mixed-integer second order cone programming. We compare the performance of a schedule generated by the proposed model to the published schedule for a major U.S. airline. On the average, there exists a 20% overall operational cost saving compared to the published schedule. To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially.     

区域航空市场航线客流量预测研究

为了有效地控制和合理地分配区域航空市场航线客流量,提高航空机场的效率,为航管决策部门提供制定计划的理论依据,在深入研究国内外航空客流量预测研究成果基础之上,针对区域航空市场的特点,提出了一个自顶向下的航线客流量预测模型。它包括总体趋势预测、中长期预测模型和短期预测模型三部分;并将神经网络和支持向量机构成的组合模型引入中长期预测模型中,使用神经网络实现短期预测模型;并结合A公司实际进行了实证研究,证明了该预测模型的有效性。文中研究成果对所有航管部门具有一定的指导意义。     

Robust crew pairing for managing extra flights

This paper discusses a modeling approach to robust crew pairing when a set of extra flights is likely to be added to the regular flight schedule. The set of these possible extra flights is known at the planning stage. We demonstrate that these extra flights may be incorporated into the schedule if necessary by modifying the planned crew pairings appropriately and without delaying or canceling existing flights. To this end, we either identify a pair of crews whose schedules may be (partially) swapped while adding an extra flight into the schedule or show that an extra flight may be inserted into the schedule of a crew without affecting others. We note that deadheading may be necessary in either case. For these two types of solutions, we define the appropriate feasibility rules with respect to the common airline regulations. We then propose two robust mathematical programming models that consider incorporating such solutions into the set of selected pairings while keeping the increase in the crew cost at an acceptable level. The baseline solution for comparison is found by a conventional crew pairing model in the literature which ignores robustness at the planning stage and relies on recovery procedures at the time of operation. We also propose the variations of the two models, where the double counting of the possible solutions across extra flights is prevented. Finally, we conduct computational experiments on a set of data generated from the actual data of an airline company. We solve the crew pairing problem both with the proposed robust models and the conventional model. Our results demonstrate the benefits of the proposed modeling approach and indicate that the proposed robust models provide natural options to recovery without disrupting the existing flights at a relatively small incremental cost, which is visible at the planning stage.     

An iterative approach to robust and integrated aircraft routing and crew scheduling

In airline scheduling a variety of planning and operational decision problems have to be solved. We consider the problems aircraft routing and crew pairing: aircraft and crew must be allocated to flights in a schedule in a minimal cost way. Although these problems are not independent, they are usually formulated as independent mathematical optimisation models and solved sequentially. This approach might lead to a suboptimal allocation of aircraft and crew, since a solution of one of the problems may restrict the set of feasible solutions of the problem solved later. Also, when minimal cost solutions are used in operations, a short delay of one flight can cause very severe disruptions of the schedule later in the day. We generate solutions that incur small costs and are also robust to typical stochastic variability in airline operations. We solve the two original problems iteratively. Starting from a minimal cost solution, we produce a series of solutions which are increasingly robust. Using data from domestic airline schedules we evaluate the benefits of the approach as well as the trade-off between cost and robustness. We extend our approach considering the aircraft routing problem together with two crew pairing problems, one for technical crew and one for flight attendants.     

Flight operations recovery: New approaches considering passenger recovery

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability. Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.     

Disruption management in the airline industry—Concepts,models and methods

This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems. A brief overview of research within schedule robustness in airline scheduling is included in the review, since this proactive measure is a natural complement to disruption management.     

Codeshare-Allianzen bei Fluggesellschaften

The paper juxtaposes the challenges that airline codeshare alliances create for analytical information systems on the one hand and their motivation from a marketing perspective on the other. The authors review the state-of-the-art literature on potential marketing benefits and analyze the impact on airline planning systems. In this regard, revenue management systems are of particular interest. Based on a simulation study, the authors infer a severe impact of decentralized codeshare controls as currently widely implemented in the industry on revenue management performance. In the scenarios examined, complementary codesharing reduces alliance-wide revenues by up to 1 %. Losses increase when a carrier experiences high local demand or a high degree of codeshare demand, and disseminate over the whole network. Virtual codeshares also cause losses of 0.3 % to 1.5 % depending on the discount level offered by the marketing carrier and on the demand structure. Finally, the authors formulate a set of managerial implications based on these findings.     

A multi-objective simulated annealing for the multi-criteria dial a ride problem

The paper describes a multi-objective mathematical model for Dial a Ride Problem (DRP) and an application of Multi-Objective Simulated Annealing (MOSA) to solve it. DRP is to take over the passenger from a place of departure to a place of arrival. In the DRP, customers send transportation requests to an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. The DRP is classified as NP-hard problem that's why most research has been concentrated on the use of approximate methods to solve it. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using the MOSA algorithm. Tests show competitive results on (Cordeau and Laporte, 2003a) benchmark datasets while improving processing times.     

A simultaneous route and departure time choice equilibrium model on dynamic networks

In this paper, we are concerned with modeling dynamic networks, when drivers simultaneously optimize their departure time and route choice. We state equilibrium conditions and propose a simulation-based model that can solve large networks accounting for many realities of actual networks. The main components of the model are a time-dependent shortest path algorithm for fixed arrival times and a traffic simulator. The proposed model has the potential to realistically capture user decisions when arrival time based origin–destination tables are easier to obtain than the departure time based ones e.g. in the morning peak and in special events. Two solution methodologies are designed and tested: the first emulates users day-to-day dynamic behavior and does not guarantee convergence; the second is a heuristic approach that adjusts link travel times and always converges to an equilibrium solution, although not at the desired level of schedule delay. Computational experiments on a small street network are presented.     

Combined Activity/Travel Choice Models: Time-Dependent and Dynamic Versions

To fully understand and predict travel demand and traffic flow, it is necessary to investigate what drives people to travel. The analysis should examine why, where and when various activities are engaged in, and how activity engagement is related to the spatial and institutional organization of an urban area. In view of this, two combined activity/travel choice models are presented in this paper. The first one is a time-dependent (quasi-dynamic) model for long-term transport planning such as travel demand forecasting, while the other one is a dynamic model for short-term traffic management such as instantaneous flow analysis. The time-dependent model is formulated as a mathematical programming problem for modeling the multinomial logit activity/destination choice and the user equilibrium route choice behavior. It can further be converted to a variational inequality problem. On the other hand, the dynamic model is aimed to find a solution for equilibrium activity location, travel route and departure time choices in queuing networks with multiple commuter classes. It is formulated as a discrete-time, finite-dimensional variational inequality and then converted to an equivalent zero-extreme value minimization problem. Solution algorithms are proposed for these two models and numerical example is presented for the latter. It is shown that the proposed modeling approaches, either based on time-dependent or dynamic traffic assignment principles, provide powerful tools to a wide variety of activity/travel choice problems in dynamic domain.     

A new conceptual structure for travel information

This paper describes how to improve dynamic transport timetables. Mostly, such information as departure time, gate number, platform number, intermediate stops, and delays is arranged per flight or train. Each train or flight has one line or one column. A field observation of passengers using such a system showed that presenting information in this fashion is not optimal. Of passengers, 38% were unable to find the correct departure time. We analysed the performance of passengers. This analysis suggested that the information should not be arranged per train or flight but per destination. Each train or flight has one line or one column. An empirical comparison supported this conclusion. When a destination-based structure was used, the number of correct answers was 16% higher, the delay of each passenger was 75% less, and the time needed to search for a train decreased by 42%.     

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009) [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

Reducing airport gate blockage in passenger aviation: Models and analysis

Commercial flights are typically assigned to an arrival gate at their destination station (airport) prior to their departure from their origin station. Although the gate is scheduled to be available when the flight arrives, this is not always the case in practice. Due to variability in departure and flight times, the arriving flight might arrive early, the previous flight departing from the gate might depart late, or both. When a flight arrives at its scheduled gate but has to wait because the preceding aircraft is still occupying that gate, we refer to this as gate blockage. Gate blockage can have many negative impacts, including passenger delays, missed connections, and increased fuel burn. Our research is focused on incorporating the inherent stochasticity of the system into the planning process to reduce the prevalence and impact of gate blockage. Specifically, we formulate an optimization problem to assign flights to gates so as to minimize the expected impact of gate blockage. We use historical data to predict delay distributions and conduct experiments to assess both the computational tractability of our approach and its potential for improvement in solution quality over existing approaches.     

Hybrid nesting control strategy for passenger railway with one-seat-one-ticket restriction

Nesting control is one of the most prevalent quantity-based controls for the revenue management problem. A popular nesting control strategy for multi-resource problem is the virtual nesting control, which sets nested booking limits on each resource. However, this control was originally developed for the airline and cannot be directly used in the passenger railway with one-seat-one-ticket restriction. Therefore, this paper develops a new nesting control that is applicable to the railway. The proposed control can nest the capacity over different fare classes and origin-destination pairs, which overcomes the shortcomings of existing railway booking-limit controls. Numerical experiments are conducted in various scenarios to evaluate the performance. The results show that the hybrid nesting control outperforms the others in all situations. In addition, the revenue improvement increases with the randomness of demand and discount percentage.     

Drivers' Mental Representation of Travel Time and Departure Time Choice in Uncertain Traffic Network Conditions

The decisions drivers make, such as choice of route or departure time, constitute typical decision making under uncertainty. Drivers' decision making has been studied within the framework of expected utility theory. However, empirical decisional phenomena violating the premise of expected utility theory have been observed repeatedly. These findings have indicated that decision making is critically affected by the decision frame. It has also been pointed out that the uncertainty of outcome is perceived as an interval of possible resultant values. Based on these findings, we propose hypotheses that: (1) a driver perceives an uncertain travel time as an interval, and (2) a driver decides on a departure time based on a decision frame edited by this interval. To test these hypotheses, we collected data on drivers' departure time choice behavior, n = 335. Decisional phenomena found in this study confirm our hypotheses.     

基于灰色神经网络的航线客流量预测

准确预测航线客流量对于航空公司制定航线销售政策有着重要的作用。现有研究中鲜见考虑民航旅客出行的随机性、客流量表现出的非线性特征以及对航线客流量影响因素的分析。针对以上问题,提出一种基于灰色神经网络的航线客流量预测模型。该模型运用灰色理论弱化数据序列的随机性,再结合非线性处理能力较强的BP神经网络,构建基于灰色神经网络的航线客流量预测模型。同时验证了平均折扣率对航线客流量的影响。实验结果表明,相比于灰色GM(1,2)模型、BP神经网络模型,灰色神经网络模型具有更高的航线客流量预测精度和更强的稳定性。